# import pandas as pd
# import matplotlib.pyplot as plt
# import numpy as np
# import torch
#
# data = pd.read_excel("files/data.xlsx")
# # 将data转化成numpy的格式处理，创建一个新的DataFrame对象
# df = pd.DataFrame(data)
#
# types = ['AD','CN','EMCI','LMCI']
# # 根据需要进行二分类的类别标签将数据找出来
#
# # 选取除了最后一列的所有列
# x_data = df.iloc[:, :-1].values
# # 选取最后一列
# y_data = df.iloc[:, -1].values
#
#
# print(x_data)
# print(y_data)

# import torch
# import torch.nn as nn
#
# # 定义一个线性层，输入特征数为10，输出特征数为5
# linear_layer = nn.Linear(in_features=90, out_features=2)
#
# # 创建一个随机的输入张量，批量大小为3，特征数为10
# input_tensor = torch.randn(3, 90)
#
# # 将输入张量通过线性层
# output_tensor = linear_layer(input_tensor)
#
# print("输入张量：")
# print(input_tensor)
# print("输出张量：")
# print(output_tensor)
#